{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T20:17:09Z","timestamp":1773260229388,"version":"3.50.1"},"reference-count":78,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funda\u00e7\u00e3o Ensino e Cultura Fernando Pessoa (FECFP)"},{"name":"Artificial Intelligence and Computer Science Laboratory, LIACC"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>The progress of artificial intelligence algorithms in digital image processing and automatic diagnosis studies of the eye disease glaucoma has been growing and presenting essential advances to guarantee better clinical care for the population. Given the context, this article describes the main types of glaucoma, traditional forms of diagnosis, and presents the global epidemiology of the disease. Furthermore, it explores how studies using artificial intelligence algorithms have been investigated as possible tools to aid in the early diagnosis of this pathology through population screening. Therefore, the related work section presents the main studies and methodologies used in the automatic classification of glaucoma from digital fundus images and artificial intelligence algorithms, as well as the main databases containing images labeled for glaucoma and publicly available for the training of machine learning algorithms.<\/jats:p>","DOI":"10.3390\/diagnostics14050530","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T11:19:24Z","timestamp":1709291964000},"page":"530","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Advancements in Glaucoma Diagnosis: The Role of AI in Medical Imaging"],"prefix":"10.3390","volume":"14","author":[{"given":"Clerimar Paulo","family":"Bragan\u00e7a","sequence":"first","affiliation":[{"name":"ISUS Unit, Faculty of Science and Technology, University Fernando Pessoa, 4249-004 Porto, Portugal"},{"name":"Department of Ophthalmology, Eye Hospital of Southern Minas Gerais State, Rua Joaquim Rosa 14, Itanhandu 37464-000, MG, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8280-1324","authenticated-orcid":false,"given":"Jos\u00e9 Manuel","family":"Torres","sequence":"additional","affiliation":[{"name":"ISUS Unit, Faculty of Science and Technology, University Fernando Pessoa, 4249-004 Porto, Portugal"},{"name":"Artificial Intelligence and Computer Science Laboratory, LIACC, University of Porto, 4100-000 Porto, Portugal"}]},{"given":"Luciano Oliveira","family":"Macedo","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Eye Hospital of Southern Minas Gerais State, Rua Joaquim Rosa 14, Itanhandu 37464-000, MG, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0382-879X","authenticated-orcid":false,"given":"Christophe Pinto de Almeida","family":"Soares","sequence":"additional","affiliation":[{"name":"ISUS Unit, Faculty of Science and Technology, University Fernando Pessoa, 4249-004 Porto, Portugal"},{"name":"Artificial Intelligence and Computer Science Laboratory, LIACC, University of Porto, 4100-000 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1097\/ICU.0000000000000649","article-title":"Current opinion in ophthalmology","volume":"31","author":"Tan","year":"2020","journal-title":"Curr. 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